This paper proposes FedCAMP-IDS, a Federated Cluster-Aware Memory-Augmented Prototypical Network for privacy-preserving intrusion detection in distributed network environments, and integrates Cluster-Aware Contrastive Pretraining, memory-augmented few-shot prototypical learning, adaptive prototype mixing, and Extreme V...
Cloud-centric internet of things (IoT) environments continuously generates heterogeneous network traffic, making intrusion detection increasingly challenging as attack behaviors evolve beyond previously observed patterns. Conventional intrusion detection systems (IDS) rely primarily on supervised classification, limiti...
Edge computing has emerged as a critical computing paradigm in modern distributed systems by migrating data processing closer to end users and Internet of Things (IoT) devices. While this paradigm decentralizes processes, minimizes latency, and reduces backhaul bandwidth congestion, it exponentially enlarges the cybera...
The Adaptive Confidence-Gated Ensemble framework for Network Intrusion Detection Systems (NIDSs) in resource-heterogeneous fog-IoT deployments is presented and targeted data collection, few-shot adaptation, and federated learning are recommended as the most critical future directions.
With the proliferation of intelligent connected vehicles, the Controller Area Network (CAN) bus, as the backbone of in-vehicle communication, is vulnerable to cyberattacks due to lack of authentication and encryption. Existing Intrusion Detection Systems (IDS) exhibit limitations in addressing data imbalance, complex a...
In wireless sensor networks (WSNs) and edge computing architectures for cyber-physical systems, Man-in-the-Middle (MIM) routing attacks present a severe security vulnerability. While continuous per-packet cryptographic encryption guarantees payload integrity, it exhausts node battery resources due to heavy computationa...
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